The rise of consensus methods in paramedicine research: A bibliographic analysis
Bibliographic record
Abstract
INTRODUCTION: Consensus-based studies are increasingly common in paramedicine research. Whilst there are four main consensus methodologies, recent analyses in other disciplines describe great diversity in method characterised by frequent modifications. AIM: To describe the application and characteristics of consensus research methodologies in paramedicine. METHODS: A bibliographic analysis was conducted of published research reporting use of a consensus methodology, drawing data from MEDLINE, EMBASE, CINAHL. Two researchers performed abstract screening, full text review, and data extraction. A descriptive analysis was conducted. RESULTS: There were 161 paramedicine consensus studies published between 1997 and 2024. Delphi technique was most frequent (83 %), followed by NGT (12 %). The US accounted for the most studies with 44 (26 %), followed by UK with 33 (20 %), Canada 15 (9 %), Norway 12 (7 %) and Australia 12 (7 %). Modifications were reported by authors in 54 % of studies. Of 141 Delphi studies, 31 % demonstrated the use of published reporting or methodological guidance. CONCLUSION: The prevalence of consensus research has increased considerably, dominated by Delphi methodology. Significant methodological heterogeneity was observed, and engagement with methodological and reporting guidelines appeared uncommon. There may be a need for stronger methodological guidance within the paramedicine research space to ensure quality in consensus research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.418 | 0.656 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.105 | 0.128 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".